RESEARCH METHOD
METHODThe XTIANZ Signal Method: How to Track AI Without Drowning in Headlines
A repeatable research method for turning AI announcements, specifications, filings, product docs, local infrastructure news, and market data into a small set of evidence-backed signals.
The six-step signal pipeline
This process is intentionally simple. The discipline comes from applying it consistently across model releases, MCP changes, company earnings, data-center projects, and market claims.
Use a source hierarchy
| Tier | Source | How XTIANZ uses it |
|---|---|---|
| Tier 1 | Specification, regulator, SEC filing, government record | Authoritative for the fact it governs |
| Tier 2 | Company documentation, investor relations, product release | Primary company evidence |
| Tier 3 | High-quality reporting or research | Context, independent reporting |
| Tier 4 | Aggregators, social posts, summaries | Discovery only unless independently verified |
A source can be authoritative about one question and weak for another. A company release is primary evidence that the company announced a product; it is not independent proof that every performance claim will hold in your environment.
Separate claim types
Observed fact: a specification was released, a filing reports a number, a county approved an action. Interpretation: what that fact means for architecture, adoption, or markets. Forecast: what may happen next. XTIANZ labels these mentally even when the article is written in natural prose.
The confidence label refers to the support for the conclusion, not to certainty about the future.
Example: an MCP release
Discovery: a new MCP specification is announced. Verification: read the official specification and changelog. Classification: “2026-07-28 is the current specification” is a fact; “the stateless core should simplify some HTTP deployments” is an interpretation that must be tested in a specific architecture. Connection: protocol and integration layer. Invalidation: implementation evidence may show that migration complexity is higher than expected.
Corrections and review dates
Fast-changing AI content ages quickly. Every flagship XTIANZ guide now shows a manual review date, author, source status, and correction path. When a material fact changes, the article should be updated and the review history should say what changed.
This release also removes several thin search-oriented pages from indexing rather than trying to preserve them for traffic. The editorial goal is usefulness and traceability, not page count.
Review history
August 9, 2026 — Reworked as a flagship XTIANZ guide with current primary sources, original decision frameworks, and technical review.
Suggest a correction ↗Disclosure
AI tools may assist research organization, drafting, code, and quality checks. The final structure, claims, frameworks, and publication decision are manually reviewed. XTIANZ does not accept payment to change technical conclusions.